Thinning: bug fixes

There are two different “namespaces” referring to module names:
normalized and de-normalized.
Normalized module names are module names that have the same
format for both data-parallel and data-serial models.
De-normalized module names are the “raw” PyTorch module names
that reflect the full model graph.  So if there is a container module
such as nn.DataParallel in the model, then a sub-module’s name
will have the “module” substring somewhere in it.

SummaryGraph operates by converting the PyTorch to ONNX, and
I’ve have issues handling nn.DataParallel in this process.
Therefore, SummaryGraph uses only normalized names internally.

PruningRecipe, on the other hand, uses de-normalized names
because it needs to operate on the model itself.

This is a sticky situation that can create really annoying bugs and makes
for some ugly code.  Nonetheless, this is the best I can do right now,
and I’ll probably revisit this soon to make it nicer.
For now, I’m pushing this commit that fixes the distinction between the
two namespaces, and fixes related bugs – in the hope that it is not too
brittle.

append_module_directive – now uses denormalize_module_name to
ensure recipe module names are denormalized.

append_param_directive – because we are dealing with parameters,
I can’t use denormalize_module_name as easily as in append_module_directive.
The clean solution is kept for later :-(